Datasets for paper "Microstructural features governing fracture of a two-dimensional amorphous solid identified by machine learning".
We applied isotropic dilational strain to a densely packed monolayer of attractive colloidal microspheres, resulting in fracture. Using brightfield microscopy and particle tracking, it is possible to examine the microstructural evolution of the monolayer during fracturing. Furthermore, using a quantified representation of the microstructure in combination with a machine learning algorithm, we calculate the likelihood of regions of the monolayer to be on a crack line.
The raw data from 20 independent experiments are provided here. The original datasets were sliced for ease of handling the file size, resulting in 79 separate files included here. Each of the 79 datasets consists of two files:
(1) particle trajectories of all particles in the monolayer
(2) classification labels for Machine Learning: particles receive a label '0' if the do not participate in the fracture, or '1' if they do.
The names of both files (1) and (2) begin with the same string, containing information on the experiment, and end with 'positions' and 'labels' respectively. A README file containing all details accompanies the datasets.
" authors: - family-names: Huisman given-names: Max - family-names: Huerre given-names: Axel - family-names: Saha given-names: Saikat orcid: "https://orcid.org/0000-0003-4136-230X" - family-names: Crocker given-names: John C. - family-names: Garbin given-names: Valeria orcid: "https://orcid.org/0000-0002-0887-500X" title: "Data underlying the publication: Microstructural features governing fracture of a two-dimensional amorphous solid identified by machine learning" keywords: version: 1 identifiers: - type: doi value: 10.4121/c4883858-a901-4e93-b716-2869a664acb0.v1 license: CC BY 4.0 date-released: 2024-08-22